A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy
Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/9/669 |
| _version_ | 1811184093872783360 |
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| author | Hongjun Guan Zongli Dai Shuang Guan Aiwu Zhao |
| author_facet | Hongjun Guan Zongli Dai Shuang Guan Aiwu Zhao |
| author_sort | Hongjun Guan |
| collection | DOAJ |
| description | Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality. |
| first_indexed | 2024-04-11T13:08:32Z |
| format | Article |
| id | doaj.art-a1640a982b6148ca86809ba0252f9fa1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:08:32Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a1640a982b6148ca86809ba0252f9fa12022-12-22T04:22:40ZengMDPI AGEntropy1099-43002018-09-0120966910.3390/e20090669e20090669A Forecasting Model Based on High-Order Fluctuation Trends and Information EntropyHongjun Guan0Zongli Dai1Shuang Guan2Aiwu Zhao3School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, ChinaRensselaer Polytechnic Institute, Troy, NY 12180, USASchool of Management, Jiangsu University, Zhenjiang 212013, ChinaMost existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.http://www.mdpi.com/1099-4300/20/9/669high-order fluctuation trendsforecastinginformation entropyneutrosophic sets |
| spellingShingle | Hongjun Guan Zongli Dai Shuang Guan Aiwu Zhao A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy Entropy high-order fluctuation trends forecasting information entropy neutrosophic sets |
| title | A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy |
| title_full | A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy |
| title_fullStr | A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy |
| title_full_unstemmed | A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy |
| title_short | A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy |
| title_sort | forecasting model based on high order fluctuation trends and information entropy |
| topic | high-order fluctuation trends forecasting information entropy neutrosophic sets |
| url | http://www.mdpi.com/1099-4300/20/9/669 |
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